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Abstract. Sedimentary records indicate that atmospheric dust has increased substantially since preindustrial times. However, state-of-the-art global Earth system models (ESMs) are unable to capture this historical increase, posing challenges in assessing the impacts of desert dust on Earth's climate. To address this issue, we construct a globally gridded dust emission dataset (DustCOMMv1) spanning 1841–2000. We do so by combining 19 sedimentary records of dust deposition with observational and modeling constraints on the modern-day dust cycle. The derived emission dataset contains interdecadal variability of dust emissions as forced by the deposition flux records, which increased by approximately 50 % from 1851–1870 to 1981–2000. We further provide future dust emission datasets for 2000–2100 by assuming three possible scenarios for how future dust emissions will evolve. We evaluate the historical dust emission dataset and illustrate its effectiveness in enforcing a historical dust increase in ESMs by conducting a long-term (1851–2000) dust cycle simulation with the Community Earth System Model (CESM2). The simulated dust depositions are in reasonable agreement with the long-term increase in most sedimentary dust deposition records and with measured long-term trends in dust concentration at sites in Miami and Barbados. This contrasts with the CESM2 simulations using a process-based dust emission scheme and with simulations from the Coupled Model Intercomparison Project (CMIP6), which show little to no secular trends in dust deposition, concentration, and optical depth. The DustCOMM emissions thus enable ESMs to account for the historical radiative forcings (RFs), including due to dust direct interactions with radiation (direct RF). Our CESM2 simulations estimate a 1981–2000 minus 1851–1870 direct RF of −0.10 W m−2 by dust aerosols up to 10 µm in diameter (PM10) at the top of atmosphere (TOA). This global dust emission dataset thus enables models to more accurately account for historical aerosol forcings, thereby improving climate change projections such as those in the Intergovernmental Panel on Climate Change (IPCC) assessment reports. 

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes. 

Abstract. Desert dust is an important atmospheric aerosol that affects the Earth's climate, biogeochemistry, and air quality. However, current Earth system models (ESMs) struggle to accurately capture the impact of dust on the Earth's climate and ecosystems, in part because these models lack several essential aeolian processes that couple dust with climate and land surface processes. In this study, we address this issue by implementing several new parameterizations of aeolian processes detailed in our companion paper in the Community Earth System Model version 2 (CESM2). These processes include (1) incorporating a simplified soil particle size representation to calculate the dust emission threshold friction velocity, (2) accounting for the drag partition effect of rocks and vegetation in reducing wind stress on erodible soils, (3) accounting for the intermittency of dust emissions due to unresolved turbulent wind fluctuations, and (4) correcting the spatial variability of simulated dust emissions from native to higher spatial resolutions on spatiotemporal dust variability. Our results show that the modified dust emission scheme significantly reduces the model bias against observations compared with the default scheme and improves the correlation against observations of multiple key dust variables such as dust aerosol optical depth (DAOD), surface particulate matter (PM) concentration, and deposition flux. Our scheme's dust also correlates strongly with various meteorological and land surface variables, implying higher sensitivity of dust to future climate change than other schemes' dust. These findings highlight the importance of including additional aeolian processes for improving the performance of ESM aerosol simulations and potentially enhancing model assessments of how dust impacts climate and ecosystem changes. 

Abstract. Aerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers. 

Abstract. Desert dust accounts for most of the atmosphere's aerosol burden by mass andproduces numerous important impacts on the Earth system. However, currentglobal climate models (GCMs) and land-surface models (LSMs) struggle toaccurately represent key dust emission processes, in part because ofinadequate representations of soil particle sizes that affect the dustemission threshold, surface roughness elements that absorb wind momentum,and boundary-layer characteristics that control wind fluctuations.Furthermore, because dust emission is driven by small-scale (∼ 1 km or smaller) processes, simulating the global cycle of desert dust inGCMs with coarse horizontal resolutions (∼ 100 km) presents afundamental challenge. This representation problem is exacerbated by dustemission fluxes scaling nonlinearly with wind speed above a threshold windspeed that is sensitive to land-surface characteristics. Here, we addressthese fundamental problems underlying the simulation of dust emissions inGCMs and LSMs by developing improved descriptions of (1) the effect of soiltexture on the dust emission threshold, (2) the effects of nonerodibleroughness elements (both rocks and green vegetation) on the surface windstress, and (3) the effects of boundary-layer turbulence on drivingintermittent dust emissions. We then use the resulting revised dust emissionparameterization to simulate global dust emissions in a standalone modelforced by reanalysis meteorology and land-surface fields. We further propose(4) a simple methodology to rescale lower-resolution dust emissionsimulations to match the spatial variability of higher-resolution emissionsimulations in GCMs. The resulting dust emission simulation showssubstantially improved agreement against regional dust emissionsobservationally constrained by inverse modeling. We thus find that ourrevised dust emission parameterization can substantially improve dustemission simulations in GCMs and LSMs. 

Abstract The role of manganese (Mn) in ecosystem carbon (C) biogeochemical cycling is gaining increasing attention. While soil Mn is mainly derived from bedrock, atmospheric deposition could be a major source of Mn to surface soils, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g., industrialization and land‐use change due to agriculture), transport, and deposition, remains uncertain. Here, we use compiled emission data sets for each identified source to model and quantify the atmospheric Mn cycle by combining an atmospheric model and in situ atmospheric concentration measurements. We estimated global emissions of atmospheric Mn in aerosols (<10 μm in aerodynamic diameter) to be 1,400 Gg Mn year⁻¹. Approximately 31% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened Mn “pseudo” turnover times in 1‐m‐thick surface soils (ranging from 1,000 to over 10,000,000 years) by 1–2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn‐to‐N ratio of the atmospheric deposition in non‐desert dominated regions (between 5 × 10⁻⁵and 0.02) across industrialized areas, but that was still lower than soil Mn‐to‐N ratio by 1–3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, consisting with atmospheric Mn deposition enhancing carbon respiration as seen in in situ biogeochemical studies. 

Abstract. Even though desert dust is the most abundant aerosol bymass in Earth's atmosphere, the relative contributions of the world's majorsource regions to the global dust cycle remain poorly constrained. Thisproblem hinders accounting for the potentially large impact of regionaldifferences in dust properties on clouds, the Earth's energy balance, andterrestrial and marine biogeochemical cycles. Here, we constrain thecontribution of each of the world's main dust source regions to the globaldust cycle. We use an analytical framework that integrates an ensemble ofglobal aerosol model simulations with observationally informed constraintson the dust size distribution, extinction efficiency, and regional dustaerosol optical depth (DAOD). We obtain a dataset that constrains therelative contribution of nine major source regions to size-resolveddust emission, atmospheric loading, DAOD, concentration, and depositionflux. We find that the 22–29 Tg (1 standard error range) global loading ofdust with a geometric diameter up to 20 µm is partitioned as follows:North African source regions contribute ∼ 50 % (11–15 Tg),Asian source regions contribute ∼ 40 % (8–13 Tg), and NorthAmerican and Southern Hemisphere regions contribute ∼ 10 %(1.8–3.2 Tg). These results suggest that current models on averageoverestimate the contribution of North African sources to atmospheric dustloading at ∼ 65 %, while underestimating the contribution ofAsian dust at ∼ 30 %. Our results further show that eachsource region's dust loading peaks in local spring and summer, which ispartially driven by increased dust lifetime in those seasons. We alsoquantify the dust deposition flux to the Amazon rainforest to be∼ 10 Tg yr−1, which is a factor of 2–3 less than inferred fromsatellite data by previous work that likely overestimated dust deposition byunderestimating the dust mass extinction efficiency. The data obtained inthis paper can be used to obtain improved constraints on dust impacts onclouds, climate, biogeochemical cycles, and other parts of the Earth system. 

Abstract. Even though desert dust is the most abundant aerosol bymass in Earth's atmosphere, atmospheric models struggle to accuratelyrepresent its spatial and temporal distribution. These model errors arepartially caused by fundamental difficulties in simulating dust emission incoarse-resolution models and in accurately representing dust microphysicalproperties. Here we mitigate these problems by developing a new methodologythat yields an improved representation of the global dust cycle. We presentan analytical framework that uses inverse modeling to integrate an ensembleof global model simulations with observational constraints on the dust sizedistribution, extinction efficiency, and regional dust aerosol opticaldepth. We then compare the inverse model results against independentmeasurements of dust surface concentration and deposition flux and find thaterrors are reduced by approximately a factor of 2 relative to currentmodel simulations of the Northern Hemisphere dust cycle. The inverse modelresults show smaller improvements in the less dusty Southern Hemisphere,most likely because both the model simulations and the observationalconstraints used in the inverse model are less accurate. On a global basis,we find that the emission flux of dust with a geometric diameter up to 20 µm (PM20) is approximately 5000 Tg yr−1, which is greater than mostmodels account for. This larger PM20 dust flux is needed to matchobservational constraints showing a large atmospheric loading of coarsedust. We obtain gridded datasets of dust emission, vertically integratedloading, dust aerosol optical depth, (surface) concentration, and wet anddry deposition fluxes that are resolved by season and particle size. As ourresults indicate that this dataset is more accurate than current modelsimulations and the MERRA-2 dust reanalysis product, it can be used toimprove quantifications of dust impacts on the Earth system.